Contents

Overview

Infrastructure systems are a cornerstone of civilization. Damage to infrastructure from natural disasters such as an earthquake (e.g. Haiti, Japan), a hurricane (e.g. Katrina, Sandy) or a flood (e.g. Kashmir floods) can lead to significant economic loss and societal suffering. Human coordination and information exchange are at the center of damage control. This project seeks to radically reform decision support systems for managing rapidly changing disaster situations by the integrated exploitation of social, physical and hazard modeling capabilities. This effort is expected to provide a model for highly integrative and collaborative work among researchers in computer science, engineering, natural sciences and the social sciences for research, education, and training of undergraduate and graduate students including those from under-represented groups.

The team will design novel, multi-dimensional cross-modal aggregation and inference methods to compensate for the uneven coverage of sensing modalities across an affected region. By assimilating data from social and physical sensors and their integrated modeling and analysis, methodology to predict and help prioritize the temporally and conceptually extended consequences of damage to people, civil infrastructure (transportation, power, waterways) and their components (e.g. bridges, traffic signals) will be designed. The team will develop innovative technology to support the identification of new background knowledge and structured data to improve object extraction, location identification, correlation or integration of relevant data across multiple sources and modalities (social, physical and Web). Novel coupling of socio-linguistic and network analysis will be used to identify important persons and objects, statistical and factual knowledge about traffic and transportation networks, and their impact on hazard models (e.g. storm surge) and flood mapping. Domain-grounded mechanisms will be developed to address pervasive trustworthiness and reliability concerns. Exemplar outcomes are expected to include specific tools for first-responders as well as recovery teams to aid in the prioritization of relief and repair efforts, leveraging improved flood response, urban mapping, and dynamic storm surge models, and interdisciplinary training of students leveraging research in pedagogy, in conjunction with Ohio State University’s new undergraduate major in data analytics, and Wright State University’s Big and Smart Data graduate certificate program.

Research Questions

Key questions that will be addressed in this project are:

How can we extract reliable, trustworthy and relevant nuggets of information related to civil infrastructure from language-based citizen sensed data sets across the stages of the disaster life-cycle (preparedness, response & recovery)?

How can we develop adaptive and dynamic models of hurricane storm surge and flood resiliency coupled with fused information from citizen sensed and remote sensed data?

What kind of interface and tools can assist first-responders in leveraging physical sensing (e.g. remote sensing), citizen sensing and their interactions pertaining to infrastructure elements (e.g. chokepoints in a traffic network due to road or bridge closures).

Jacob Ross, Krishnaprasad Thirunarayan (2016). Features for Ranking Tweets Based on Credibility and Newsworthiness. 17th International Conference on Collaboration Technologies and Systems (CTS 2016). 18-25.

Tutorials

Blog Posts

Tools

Location Name Extractor (LNEx): A fine-grained geoparsing tool which extracts location mentions from texts and geocode them using OpenStreetMap. The tool was specifically designed for disaster-related use-cases to support spatio-temporal analysis of data for disaster response.

Geoann: an annotation tool for geocoding location names in texts. We use [brat.nlplab.org Brat] (a web-based tool for NLP-assisted text annotation) for visualizing the annotated texts. Geoann allows the annotator to retrieve all the required geo-information needed without leaving the annotation panel, i.e., it facilitates the retrieval and search of location names using Google Maps API. The tool then allows users to annotate location names by drawing bounding boxes of their spatial extents. Then, it saves the annotations in the same file-based stand-off format of each tweet.